Google Gemma 4 Review: The Most Capable Open Model Family You Can Run on Your Own Hardware
Google launched Gemma 4 on April 2, 2026. Four models from phone to workstation, Apache 2.0 license, Gemini 3 research inside, #3 on Arena AI. Here is everything developers need to know.
TL;DR
Gemma 4 is Google's most capable open model family, released April 2, 2026. Built from the same research as Gemini 3 and licensed under Apache 2.0. Available in four sizes spanning Android phones to developer workstations.
Effective 2B (E2B) and Effective 4B (E4B) for edge and mobile. 26B Mixture of Experts (26B MoE) and 31B Dense for laptops and workstations. The 31B Dense ranks #3 on Arena AI's open model leaderboard. The 26B MoE ranks #6.
Intelligence-per-parameter is the headline claim. The 31B model outcompetes models 20x its size on Arena AI. All four models natively process video and images. The E2B and E4B also support native audio input.
Previous Gemma generations used a custom Gemma license with restrictions. Gemma 4 moves to full Apache 2.0, meaning unrestricted commercial use, no attribution requirements, and complete data sovereignty for enterprise deployments.
Built in collaboration with Google Pixel, Qualcomm, and MediaTek. The E2B and E4B models run offline with near-zero latency on Android phones, Raspberry Pi, and Jetson Nano. 128K context window at the edge.
31B and 26B MoE in Google AI Studio. E4B and E2B in Google AI Edge Gallery. Model weights on Hugging Face, Kaggle, and Ollama. Day-one support for vLLM, llama.cpp, MLX, LM Studio, NVIDIA NIM, Docker, and more.
Google Gemma 4: The Most Capable Open Model Family You Can Run on Your Own Hardware
Google announced Gemma 4 on April 2, 2026, and it is the most significant open model release the company has ever made. Not because of raw benchmark numbers alone, but because of what the combination of Gemini 3 research, Apache 2.0 licensing, and genuine edge hardware support means for developers who have been waiting for an open model that does not force a compromise between capability and control.
The pitch is direct: "byte for byte, the most capable family of open models." Four sizes covering everything from a Raspberry Pi to a developer workstation, all running Gemini 3 research, all commercially free under Apache 2.0, all natively multimodal.
Why Gemma 4 Matters
Before getting into the model specs, it is worth understanding what changed compared to Gemma 3.
The previous Gemma generation used a custom Gemma license that restricted certain commercial uses and redistribution. Gemma 4 moves to Apache 2.0, one of the most permissive open-source licenses that exists. For enterprises, that difference is the gap between a model they can evaluate and a model they can actually ship. For developers, it means building commercial products without worrying about license compliance.
The second change is the scale of ecosystem integration. Gemma 3 had decent tooling support. Gemma 4 launches with day-one support for Hugging Face Transformers, vLLM, llama.cpp, MLX, Ollama, LM Studio, NVIDIA NIM and NeMo, Docker, MaxText, Keras, and more. That list matters because it means developers do not need to wait weeks for their preferred inference stack to add support.
The third change is the research foundation. Gemma 4 is built from the same research and technology as Gemini 3, meaning it carries the architectural improvements that made Gemini 3.1 Pro lead 13 of 16 major benchmarks in February. The Gemma family has always been described as built from Gemini research, but the generational gap between Gemma 3 and Gemma 4 is the largest yet.
The Four Model Sizes
Gemma 4 is not a single model. It is a family of four, each designed for a different hardware tier and use case.
Effective 2B (E2B): AI on the Smallest Devices
The E2B is designed for phones, IoT devices, Raspberry Pi, and NVIDIA Jetson Orin Nano. "Effective" refers to the parameter footprint during inference: the model activates an effective 2 billion parameter footprint to preserve RAM and battery life, while carrying more total parameters that enable it to punch above a standard 2B model in quality.
It was built in close collaboration with the Google Pixel team, Qualcomm, and MediaTek, specifically tuned for the hardware constraints of Android devices. It runs completely offline with near-zero latency. It supports native audio input for speech recognition, native video and image processing, and over 140 languages. Context window: 128K tokens.
For developers building Android applications, the E2B is the entry point for embedding genuine AI capability directly on the device without server dependency.
Effective 4B (E4B): Edge With More Headroom
The E4B follows the same architecture and hardware targets as the E2B, with the higher effective parameter count giving it meaningfully more capability for complex reasoning tasks. Like the E2B, it includes native audio input, vision, video, and multilingual support, and runs offline.
The E4B is the recommended starting point for developers who need more reasoning depth on device, and it fits within the hardware constraints of modern Android flagships and capable edge devices.
26B Mixture of Experts (MoE): Speed-Optimized Powerhouse
The 26B MoE is the workstation-tier model optimized for speed. Its key architectural feature: it activates only 3.8 billion of its total 26 billion parameters during inference. This is the Mixture-of-Experts efficiency that makes it deliver fast tokens-per-second on a capable laptop GPU while still carrying the knowledge capacity of a much larger model.
It currently ranks #6 on Arena AI's open model leaderboard. Google recommends it for Android Studio Agent Mode, where the speed of code generation matters more than the absolute ceiling of reasoning depth. It fits on consumer GPUs without the quantization overhead required by the 31B Dense.
Context window: 256K tokens.
31B Dense: Maximum Quality and Fine-Tuning Foundation
The 31B Dense is the flagship. It ranks #3 on Arena AI's open model text leaderboard, behind only two Chinese open models. It runs unquantized in bfloat16 on a single 80GB NVIDIA H100, which makes it deployable on a single accelerator for serious research and enterprise workloads.
The Dense architecture means all 31 billion parameters are active for every inference pass, which maximizes raw quality and makes it the best foundation for fine-tuning to specific domains. Google has already cited early use cases including a Bulgarian-first language model and Yale University's Cell2Sentence-Scale model for cancer research, both built on Gemma 4 fine-tuning.
For local setups, quantized versions of the 31B run on consumer GPUs for offline coding workflows and agentic applications.
Context window: 256K tokens.
What All Four Models Share
Regardless of size, every Gemma 4 model comes with the same core capabilities:
Native multimodal processing. All four models natively process video and images at variable resolutions, enabling OCR, chart understanding, and visual reasoning without separate vision models bolted on.
Native audio input for the E2B and E4B, enabling real-time speech recognition and understanding directly on device.
Advanced reasoning. Multi-step planning and deep logic, with significant improvements in math and instruction-following benchmarks over Gemma 3.
Agentic capabilities from day one. Native support for function calling, structured JSON output, and native system instructions. The earlier Gemma generations required developers to work around limited native function calling. Gemma 4 treats agents as a first-class use case, not an afterthought.
Offline code generation. All four models can generate high-quality code without an internet connection, turning any hardware they run on into a local-first AI code assistant.
140+ languages. Natively trained across linguistic diversity that far exceeds most open models.
Apache 2.0 license. Full commercial freedom, complete data sovereignty, no restrictions.
The Intelligence-per-Parameter Claim
Google's headline claim for Gemma 4 is intelligence-per-parameter: the idea that its models achieve frontier-level reasoning at a fraction of the parameter count of competitors that match or exceed them on leaderboards.
The evidence they point to: the 31B Dense ranks #3 on Arena AI's open model text leaderboard, and the 26B MoE ranks #6. Both outcompete models 20x their size on that leaderboard. In the open-source category on Arena AI as of April 2, 2026, the top positions are dominated by Chinese open models, with Gemma 4's 31B claiming the highest-ranked Western open model position.
This claim is backed by architectural work from Google DeepMind researchers Clement Farabet and Olivier Lacombe, who describe the efficiency gains as structural rather than just scaling. The Mixture-of-Experts approach in the 26B model, activating only 3.8B parameters per inference pass, is a concrete example of how raw model size and inference cost have been decoupled.
How to Access and Deploy Gemma 4
Google AI Studio: The 31B Dense and 26B MoE are available immediately at aistudio.google.com for experimentation with no setup required.
Google AI Edge Gallery: The E4B and E2B are available in the dedicated edge app.
Android Studio: Gemma 4 powers the Agent Mode feature for local AI coding assistance. Select Gemma 4 as your local model after installing LM Studio or Ollama and connecting them in Settings.
Hugging Face / Kaggle / Ollama: Download the model weights directly for local deployment in your preferred framework.
Google Cloud: Available on Google Kubernetes Engine (GKE) with vLLM, on Cloud Run with NVIDIA RTX PRO 6000 (Blackwell) GPUs (96GB vGPU), and on Google Cloud TPUs. The 26B MoE model is coming to Vertex AI Model Garden as a fully managed serverless deployment in the coming days.
Inference framework support: vLLM, llama.cpp, MLX, LM Studio, NVIDIA NIM and NeMo, Ollama, Docker, MaxText, Keras, Unsloth, SGLang, Transformers.js, and more.
Fine-tuning: Google Colab, Vertex AI, or your own GPU. The 31B Dense is specifically recommended as a fine-tuning foundation.
Enterprise and Sovereign Deployment
Gemma 4 is designed to meet enterprise compliance requirements that proprietary API-based models cannot satisfy.
Because you control the weights and run the model on your own infrastructure, your data never leaves your environment. Google Cloud supports Gemma 4 across all Sovereign Cloud offerings, including public cloud with Data Boundary, Google Cloud Dedicated deployments like S3NS in France, and Google Distributed Cloud for air-gapped and fully on-premises deployments.
For fine-tuning at scale, Vertex AI Training Clusters offer optimized SFT recipes and high-scale resiliency through NVIDIA NeMo Megatron. The Apache 2.0 license means that customized versions can be deployed and distributed without any license negotiations.
The GKE Agent Sandbox enables safe execution of LLM-generated code and tool calls within highly isolated, Kubernetes-native environments with sub-second cold starts and up to 300 sandboxes per second, purpose-built for multi-step agentic workflows.
Compare Gemma 4 Against Other Open Models
Wondering how Gemma 4's 31B Dense stacks up against Llama 4 Maverick, Qwen 3.5, or Kimi K2.5 on the benchmarks that matter for your specific workflow?
Compare AI models side by side on Renovate QR →
The /tools directory covers every major open and proprietary model with benchmark scores, pricing, context windows, hardware requirements, and licensing details in one place. If you are deciding whether Gemma 4's 31B or the 26B MoE fits your stack, or how it compares to what you are already running, the comparison view gets you there without reading twelve separate benchmark posts.
The Bigger Picture
Gemma 4 is Google's clearest statement yet that it is serious about competing in the open model space, not just the proprietary frontier.
The previous Gemma generations were capable but came with licensing friction and modest ecosystem integration. Gemma 4 removes the licensing friction entirely with Apache 2.0, dramatically expands the deployment ecosystem, and for the first time positions a Gemma model at the genuine top of an open model leaderboard.
The 400 million downloads the Gemma family accumulated before this release, and the 100,000-plus community variants built on top of it, signal that the developer demand was always there. What was missing was the capability and commercial freedom to match. Gemma 4 addresses both.
The edge story is particularly significant. Running Gemini 3 research on a Raspberry Pi or an Android phone offline, with native audio and vision, under Apache 2.0, is not a gimmick. It is the infrastructure layer for a generation of AI applications that do not depend on API calls, do not expose user data to cloud providers, and do not accumulate usage costs that make deployment economics unviable.
For developers evaluating their open model options in April 2026, Gemma 4 deserves serious consideration. The 31B Dense for maximum quality, the 26B MoE for speed and Android development, the E4B for capable edge deployment, and the E2B for the smallest footprint available from any major lab with this capability level.
Last updated: April 2, 2026. We will update this article as benchmark results from independent evaluators are published and as Vertex AI serverless availability is confirmed.
Frequently Asked Questions
What is Gemma 4 and how is it different from Gemini?
Gemma 4 is Google's open model family, released April 2, 2026, built from the same research and technology as Gemini 3, Google's proprietary frontier model. The key distinction is access and licensing: Gemini models are proprietary and accessed only through Google's APIs, while Gemma 4 is open-weight, meaning you can download the model weights, run them on your own hardware, fine-tune them, and deploy them however you choose. Gemma 4 is licensed under Apache 2.0, which allows full commercial use with no restrictions.
What are the four Gemma 4 model sizes and which should I use?
Gemma 4 comes in four sizes targeting different hardware. The Effective 2B (E2B) runs on phones, Raspberry Pi, and IoT devices with near-zero latency, requires minimal RAM, and includes native audio input. The Effective 4B (E4B) offers more capability at still very low memory requirements, also with native audio, and runs on Android and laptop hardware. The 26B Mixture of Experts (MoE) is optimized for latency: it activates only 3.8 billion of its total parameters during inference, giving you fast tokens-per-second on a capable laptop GPU or single H100. The 31B Dense maximizes raw quality and is the best foundation for fine-tuning, running on a single 80GB NVIDIA H100. For Android development, Google recommends the 26B MoE.
What does Apache 2.0 license mean for Gemma 4?
Apache 2.0 is one of the most permissive open-source licenses available. For Gemma 4, it means you can use the model commercially without restrictions, you do not need to open-source your own application code, you can modify and distribute the model weights, and you are not required to share fine-tuned versions. Previous Gemma generations used a custom Gemma license that imposed additional restrictions, particularly around commercial use and redistribution. The switch to Apache 2.0 is a significant upgrade for enterprise teams and developers building commercial products on top of Gemma.
Can Gemma 4 run offline on my phone?
Yes, the E2B and E4B models are specifically designed for offline, on-device inference. Google developed them in collaboration with the Google Pixel team, Qualcomm, and MediaTek. They run with near-zero latency on Android phones, Raspberry Pi, and NVIDIA Jetson Orin Nano without any internet connection. For Android development, Gemma 4 powers Agent Mode in Android Studio and can be used in production apps via the ML Kit GenAI Prompt API. The edge models feature a 128K context window.
How does Gemma 4 compare to Llama 4, Qwen 3.5, and other open models?
The 31B Dense model currently ranks #3 on Arena AI's open model text leaderboard and the 26B MoE ranks #6, with Google claiming both outcompete models 20x their size. In the open-source category on Arena AI, the top positions are dominated by Chinese open models. Gemma 4 sits alongside strong competition from Meta Llama 4 Maverick, Qwen 3.5, Kimi K2.5, and GLM-5. Gemma 4's specific advantage is its hardware efficiency: achieving near-frontier performance at parameter counts small enough to run on consumer hardware without quantization hacks.
Where can I download and try Gemma 4?
You can access Gemma 4 immediately in several ways. For the larger models (31B Dense and 26B MoE), use Google AI Studio at aistudio.google.com. For the edge models (E4B and E2B), use the Google AI Edge Gallery app. Download model weights from Hugging Face, Kaggle, or Ollama. For local deployment, the models work with vLLM, llama.cpp, MLX, LM Studio, NVIDIA NIM and NeMo, Docker, Keras, and most major inference frameworks from day one. On Google Cloud, the 26B MoE will be available as a fully managed serverless model on Vertex AI Model Garden in the coming days.
What is the context window for Gemma 4 models?
Context windows vary by model tier. The edge models (E2B and E4B) support 128K tokens, which is enough for large documents, long conversations, and medium-sized codebases. The larger models (26B MoE and 31B Dense) support 256K tokens, which lets you pass entire repositories or extensive document collections in a single prompt. This is a significant increase over previous Gemma generations and puts Gemma 4 in the same context class as Gemini 3 Pro and Claude Sonnet 4.6 for the larger tiers.
